Towards Hybrid Neural Learning Internet Agents

The following chapter explores learning internet agents. In recent years, with the massive increase in the amount of available information on the Internet, a need has arisen for being able to organize and access that data in a meaningful and directed way. Many well-explored techniques from the field of AI and machine learning have been applied in this context. In this paper, special emphasis is placed on neural network approaches in implementing a learning agent. First, various important approaches are summarized. Then, an approach for neural learning internet agents is presented, one that uses recurrent neural networks for the learning of classifying a textual stream of information. Experimental results are presented showing that a neural network model based on a recurrent plausibility network can act as a scalable, robust and useful news routing agent. concluding section examines the need for a hybrid integration of various techniques to achieve optimal results in the problem domain specified, in particular exploring the hybrid integration of Preference Moore machines and recurrent networks to extract symbolic knowledge.

[1]  Vasant Honavar,et al.  Symbolic Artificial Intelligence and Numeric Artificial Neural Networks: Towards A Resolution of the Dichotomy , 1995 .

[2]  Robert C. Holte,et al.  A Learning Apprentice For Browsing , 1994 .

[3]  Yoav Shoham,et al.  An Adaptive Agent for Automated Web Browsing , 1997 .

[4]  Miles H. Krumbine Hell , 1930, The Journal of Religion.

[5]  Timo Honkela,et al.  Self-Organizing Maps in Symbol Processing , 1998, Hybrid Neural Systems.

[6]  Jan O. Pedersen,et al.  Document Routing as Statistical Classification , 1996 .

[7]  Giles,et al.  Searching the world wide Web , 1998, Science.

[8]  J. Elman Distributed Representations, Simple Recurrent Networks, And Grammatical Structure , 1991 .

[9]  John Sum,et al.  A Note on the Equivalence of NARX and RNN , 1999, Neural Computing & Applications.

[10]  Jaideep Srivastava,et al.  Web mining: information and pattern discovery on the World Wide Web , 1997, Proceedings Ninth IEEE International Conference on Tools with Artificial Intelligence.

[11]  Teuvo Kohonen,et al.  Self-Organization of Very Large Document Collections: State of the Art , 1998 .

[12]  Ellen Riloff,et al.  A Case Study in Using Linguistic Phrases for Text Categorization on the WWW , 1998 .

[13]  Teuvo Kohonen,et al.  Self-organization and associative memory: 3rd edition , 1989 .

[14]  Ron Sun,et al.  Computational Architectures Integrating Neural And Symbolic Processes , 1994 .

[15]  David J. Spiegelhalter,et al.  Machine Learning, Neural and Statistical Classification , 2009 .

[16]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[17]  James A. Hendler,et al.  Developing Hybrid Symbolic/Connectionist Models , 1991 .

[18]  Hava T. Siegelmann,et al.  Computational capabilities of recurrent NARX neural networks , 1997, IEEE Trans. Syst. Man Cybern. Part B.

[19]  Thomas G. Dietterich What is machine learning? , 2020, Archives of Disease in Childhood.

[20]  Thorsten Joachims,et al.  Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.

[21]  T. Joachims WebWatcher : A Tour Guide for the World Wide Web , 1997 .

[22]  Sebastian Thrun,et al.  Learning to Classify Text from Labeled and Unlabeled Documents , 1998, AAAI/IAAI.

[23]  Dayne Freitag,et al.  Information Extraction from HTML: Application of a General Machine Learning Approach , 1998, AAAI/IAAI.

[24]  Prasad Tadepalli,et al.  The Use of Active Learning in Text Categorization , 1996 .

[25]  Terry R. Payne,et al.  Experience with Learning Agents which Manage Internet-Based Information , 1996 .

[26]  Oren Etzioni,et al.  Adaptive Web Sites: an AI Challenge , 1997, IJCAI.

[27]  Ron Sun,et al.  Integrating rules and connectionism for robust commonsense reasoning , 1994, Sixth-generation computer technology series.

[28]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[29]  James P. Callan,et al.  Text-Based Information Retrieval Using Exponentiated Gradient Descent , 1996, NIPS.

[30]  Risto Miikkulainen,et al.  Subsymbolic natural language processing - an integrated model of scripts, lexicon, and memory , 1993, Neural network modeling and connectionism.

[31]  Peter Tiño,et al.  Learning long-term dependencies in NARX recurrent neural networks , 1996, IEEE Trans. Neural Networks.

[32]  Stefan Wermter,et al.  Hybrid Neural Plausibility Networks for News Agents , 1999, AAAI/IAAI.

[33]  Thorsten Joachims,et al.  Web Watcher: A Tour Guide for the World Wide Web , 1997, IJCAI.

[34]  Yoav Shoham,et al.  Learning Information Retrieval Agents: Experiments with Automated Web Browsing , 1995 .

[35]  Timo Honkela,et al.  Self-Organizing Maps of Document Collections , 1996 .

[36]  Ron Sun,et al.  Multi-agent reinforcement learning: weighting and partitioning , 1999, Neural Networks.

[37]  William W. Cohen Learning Rules that Classify E-Mail , 1996 .

[38]  Timo Honkela,et al.  WEBSOM - Self-organizing maps of document collections , 1998, Neurocomputing.

[39]  Filippo Menczer,et al.  Artificial Life Applied to Adaptive Information Agents , 1995 .

[40]  Gheorghe Tecuci Building Intelligent Agents: An Apprenticeship, Multistrategy Learning Theory, Methodology, Tool and Case Studies , 1998 .

[41]  Stefan Wermter Hybrid Connectionist Natural Language Processing , 1994 .

[42]  C. Lee Giles,et al.  Extraction, Insertion and Refinement of Symbolic Rules in Dynamically Driven Recurrent Neural Networks , 1993 .

[43]  Tom M. Mitchell,et al.  Learning to Extract Symbolic Knowledge from the World Wide Web , 1998, AAAI/IAAI.

[44]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[45]  Michael I. Jordan Attractor dynamics and parallelism in a connectionist sequential machine , 1990 .